






"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()




class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Tuple      =  PriorTransformer
					lowercase_    :				Any      =  """hidden_states"""
					@property
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict:
											lowerCAmelCase   :Optional[int]						=			4
											lowerCAmelCase   :str						=			8
											lowerCAmelCase   :Optional[Any]						=			7

											lowerCAmelCase   :Tuple						=			floats_tensor((batch_size, embedding_dim)   ).to(UpperCAmelCase   )

											lowerCAmelCase   :str						=			floats_tensor((batch_size, embedding_dim)   ).to(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCAmelCase   )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Dict=0   )	->       Any:
											torch.manual_seed(UpperCAmelCase   )
											lowerCAmelCase   :str						=			4
											lowerCAmelCase   :Optional[int]						=			8
											lowerCAmelCase   :List[Any]						=			7

											lowerCAmelCase   :int						=			torch.randn((batch_size, embedding_dim)   ).to(UpperCAmelCase   )

											lowerCAmelCase   :int						=			torch.randn((batch_size, embedding_dim)   ).to(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCAmelCase   )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}
					@property
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Any:
											return (4, 8)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       str:
											return (4, 8)
					def        UpperCAmelCase__ (							self							:	Any   )	->       int:
											lowerCAmelCase   :List[Any]						=			{
											    'num_attention_heads': 2,
											    'attention_head_dim': 4,
											    'num_layers': 2,
											    'embedding_dim': 8,
											    'num_embeddings': 7,
											    'additional_embeddings': 4,
											}
											lowerCAmelCase   :Optional[int]						=			self.dummy_input
											return init_dict, inputs_dict
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			PriorTransformer.from_pretrained(
											    'hf-internal-testing/prior-dummy'     , output_loading_info=UpperCAmelCase   )
											self.assertIsNotNone(UpperCAmelCase   )
											self.assertEqual(len(loading_info['missing_keys']   )     , 0   )

											model.to(UpperCAmelCase   )
											lowerCAmelCase   :str						=			model(**self.dummy_input   )[0]

											assert hidden_states is not None, "Make sure output is not None"
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Dict:
											lowerCAmelCase	, lowerCAmelCase   :Tuple						=			self.prepare_init_args_and_inputs_for_common()

											lowerCAmelCase   :Union[str, Any]						=			self.model_class(**UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			inspect.signature(model.forward   )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase   :List[Any]						=			[*signature.parameters.keys()]

											lowerCAmelCase   :Any						=			['hidden_states', 'timestep']
											self.assertListEqual(arg_names[:2]     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       List[str]:
											lowerCAmelCase   :List[str]						=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
											lowerCAmelCase   :List[str]						=			model.to(UpperCAmelCase   )

											if hasattr(UpperCAmelCase     , 'set_default_attn_processor'   ):
																	model.set_default_attn_processor()

											lowerCAmelCase   :List[Any]						=			self.get_dummy_seed_input()

											with torch.no_grad():
																	lowerCAmelCase   :int						=			model(**UpperCAmelCase   )[0]

											lowerCAmelCase   :Optional[int]						=			output[0, :5].flatten().cpu()
											print(UpperCAmelCase   )

											# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
											# the expected output slices are not the same for CPU and GPU.
											lowerCAmelCase   :Optional[int]						=			torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]   )
											self.assertTrue(torch_all_close(UpperCAmelCase     , UpperCAmelCase     , rtol=1e-2   )   )




@slow
class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple=1     , UpperCAmelCase							:	Tuple=768     , UpperCAmelCase							:	List[str]=77     , UpperCAmelCase							:	Optional[Any]=0   )	->       Any:
											torch.manual_seed(UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			batch_size
											lowerCAmelCase   :str						=			embedding_dim
											lowerCAmelCase   :Dict						=			num_embeddings

											lowerCAmelCase   :List[Any]						=			torch.randn((batch_size, embedding_dim)   ).to(UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			torch.randn((batch_size, embedding_dim)   ).to(UpperCAmelCase   )
											lowerCAmelCase   :int						=			torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCAmelCase   )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}
					def        UpperCAmelCase__ (							self							:	str   )	->       Optional[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
					        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
					        # fmt: on
					    ]   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	int   )	->       str:
											lowerCAmelCase   :Dict						=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'     , subfolder='prior'   )
											model.to(UpperCAmelCase   )
											lowerCAmelCase   :int						=			self.get_dummy_seed_input(seed=UpperCAmelCase   )

											with torch.no_grad():
																	lowerCAmelCase   :str						=			model(**UpperCAmelCase   )[0]

											assert list(sample.shape   ) == [1, 768]

											lowerCAmelCase   :Optional[int]						=			sample[0, :8].flatten().cpu()
											print(UpperCAmelCase   )
											lowerCAmelCase   :str						=			torch.tensor(UpperCAmelCase   )

											assert torch_all_close(UpperCAmelCase     , UpperCAmelCase     , atol=1e-3   )







"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"